The diversity of nodes in a complex network causes each node to have varying significance,
and the important nodes often have a significant impact on the structure and function of the …

It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear
behaviours. Here we challenge this assumption by leveraging mathematical models derived …

Over the last twenty years, dynamic modeling of biomolecular networks has exploded in
popularity. Many of the classical tools for understanding dynamical systems are unwieldy in …

A central challenge in the computational modeling of neural dynamics is the trade-off
between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are …

We present new applications of parity inversion and time reversal to the emergence of
complex behavior from simple dynamical rules in stochastic discrete models. Our parity …

Regulatory networks as large and complex as those implicated in cell-fate choice are
expected to exhibit intricate, very high-dimensional dynamics. Cell-fate choice, however, is a …

We perform logic-based network analysis on a model of the mammalian cell cycle. This
model is composed of a Restriction Switch driving cell cycle commitment and a Phase …

Cardiac hypertrophy is a context-dependent phenomenon wherein a myriad of biochemical
and biomechanical factors regulate myocardial growth through a complex large-scale …

The dynamics of complex biological networks may be modeled in a Boolean framework,
where the state of each system component is either abundant (ON) or scarce/absent (OFF) …

Interacting biological systems at all organizational levels display emergent behavior.
Modeling these systems is made challenging by the number and variety of biological …